Best prompts for writing research papers

How AI Helps Scientists: The Best Prompts for Writing Scientific Articles

Prompts for writing scientific articles help structure work with neural networks and produce texts that are closer to academic requirements. The wording of the prompt influences the depth of analysis, the logic of argumentation, and compliance with the journal or conference format. When working with humanities texts, a well-thought-out prompt saves time on reworking the draft and reduces the risk of losing important meanings. Experience shows that a clear statement of the neural network’s task improves the quality of the final material.

A prompt is a formalized text description of the problem, which the neural network uses as the basis for generating the text. This prompt defines the topic, structure, citation format, and the required level of detail. In the industry, it is generally accepted that a prompt serves as a technical specification for the model, not simply a short command. The quality of the result is influenced by how clearly the author describes their expectations.

The importance of a well-thought-out prompt is especially noticeable when working with long texts, where it’s crucial to maintain a consistent style, logical argumentation, and sectional consistency. Neural networks use prompts as a framework for constructing a narrative, so unclear wording leads to vague conclusions and superficial analysis. This is critical for an academic paper, as editors pay attention to the consistency and clarity of argumentation.

The goal of this article is to demonstrate how to create an effective prompt for a scientific text without violating academic standards and originality requirements. An additional goal is to offer standard wording for different sections of the paper, which you can adapt to your field of research. This material is based on general recommendations for working with neural networks and experience using services like chataibot.pro in educational and research projects.

How to Use Prompts

The most convenient way to use prompts for research papers is to use them step-by-step, integrating them into the overall publication preparation process. First, the author creates a plan and defines the journal or conference requirements. Then, step by step, formulate queries for generating drafts of individual sections, editing, and proofreading. This approach allows you to maintain control over the content while speeding up the writing of complex sections.

When working with neural network-based services, it’s important to strike a balance between automation and personal input. A neural network can help with the selection of wording, examples, and structural options, but responsibility for the scientific novelty and correctness of the conclusions remains with the author.

The optimal approach is one in which the model prepares a draft text, and the researcher refines it, taking into account the requirements of the discipline and academic writing standards.

Define the goal: what exactly is needed—a draft introduction, a method description template, a definition of limitations, or style editing.
Prepare the initial data: work plan, abstract, source excerpts, journal requirements.
Break the process down into steps and set precise queries for each stage, avoiding overly general formulations.
Review the result: edit, remove potential boilerplate expressions, evaluate consistency and compliance with academic standards.

Authors use the chataibot.pro platform as a single access point to models that solve these tasks as part of a common workflow. According to public descriptions, the service allows you to upload documents, submit them to the model, and receive suggestions for structuring, editing, and finalizing the text, which is convenient when preparing large-scale scientific papers. This type of assistance is especially useful for authors who combine research with a high workload and value the ability to partially automate publication preparation.

Structure of a Scientific Text Prompt

The structure of a scientific text prompt typically follows the structure of the publication itself. The prompt should include the topic, type of work (review, original research, meta-analysis), target audience, and format requirements. In accordance with general academic standards, it is also helpful to immediately describe the desired length, style, and limitations: prohibition of fictitious data, the need to cite open sources, and an emphasis on critical analysis. This approach helps the neural network maintain a defined framework from the introduction to the conclusions.

You can also include a citation format and requirements for the bibliography. A clear distinction is usually helpful: where the theoretical part is needed, where the data analysis is needed, and where a discussion of the limitations of the study is needed. Below is an example of a basic structure that you can adapt to a specific field.

Describe the research topic, target audience, and type of work (review, original research, analytical article).
List the key concepts that are important to cover and indicate the desired section structure (introduction, methods, results, discussion, conclusions).
Set constraints: no fictitious data, with an emphasis on critical analysis, and with explanations of all specialized terms.

How to Use Prompts

The most convenient way to use prompts for research papers is to use them step-by-step, integrating them into the overall publication preparation process. First, the author creates a plan and defines the journal or conference requirements. Then, step-by-step, formulate queries for generating drafts of individual sections, editing, and proofreading. This approach allows for maintaining control over the content while speeding up the writing of complex sections.

When working with neural network-based services, it’s important to maintain a balance between automation and personal input. A neural network can help with the selection of wording, examples, and structure options, but responsibility for the scientific novelty and correctness of the conclusions remains with the author. The optimal design involves the model preparing a draft text, and the researcher revising it based on discipline requirements and academic writing standards.

Formulate the goal: what exactly is needed—a draft introduction, a method description template, a statement of limitations, or a style edit.
Prepare the initial data: a work plan, abstract, source excerpts, and journal requirements.
Break the process down into steps and set precise queries for each stage, avoiding overly general formulations.
Review the result: edit, remove potential boilerplate expressions, and assess consistency and compliance with academic standards.

The authors use the chataibot.pro platform as a single access point to models that solve these problems within a single workflow. The service allows users to upload documents, submit them to the model, and receive suggestions for structuring, editing, and refining the text, which is convenient when preparing large-scale scientific papers. This type of assistance is especially useful for authors who combine research with a high workload and value the ability to partially automate publication preparation.

Prompts for different parts of the article

Different sections of a research paper address different objectives, so their prompts should be worded differently. For the introduction, the emphasis shifts to the rationale for the paper’s relevance, goal statement, and brief description of the context. For the methods section, precision of wording, reproducibility of procedures, and a correct description of the sample are more important. The discussion section should include an interpretation of the results, a comparison with existing studies, and a note of limitations.

Using specialized prompts allows for targeted application of the neural network, rather than asking it to write the entire paper. In the industry, it’s common to break a larger task into distinct steps: first, draft the introduction, then refine the methodology, and finally formulate the conclusions. Below are examples of typical wording for key sections.

Introduction: “Generate an introduction for a research paper on the topic ‘[indicate topic]‘. Describe the context, identify the problem, purpose, and contribution of the study. Write in a formal academic style, without fictitious data. State why the problem is important to the field.”
Literature Review: “Structure a literature review on the topic ‘[indicate topic]‘. Divide the text into logical subheadings, highlighting key research areas and controversies. Use a neutral academic style, avoiding categorical statements without references.”
Methods and Results: “Describe the methodology and study design: sample, instruments, design, and data analysis. Avoid inventing numerical results, but describe the general approach. Then, suggest a template for the Results section without specific numbers.”
Discussion and Conclusions: “Form a discussion for the paper on the topic ‘[indicate topic]’ based on the results listed. Link the conclusions to existing work, identify limitations, and identify possible directions for further research. Avoid emotional evaluations.”

Prompts for Improving Scientific Style

A separate category of queries helps improve scientific texts without changing the actual content. In typical cases, the neural network acts as an editor, harmonizing the draft with a consistent style and removing colloquialisms. General recommendations suggest that the model should not add new facts or references, but only rephrase existing phrases. This approach reduces the risk of distortion and maintains the author’s control over the content.

In editing prompts, it’s helpful to clarify the target journal or audience, the desired level of terminology, and the language of the work. You can also ask the author to highlight ambiguous passages and suggest several possible reformulations. This mode allows you to gradually improve the draft and adapt it to the reviewers’ requirements.

The chataibot.pro service provides access to the neural network through three channels: the website, a Telegram bot, and a browser extension.

Important Considerations When Writing Prompts

When writing prompts for scientific texts, it’s important to consider the limitations of neural networks. Models like Chat GPT can generate coherent text, but they don’t perform full-fledged empirical research or conduct experiments. Standard practice holds the author responsible for fact-checking, accurate interpretations, and adherence to ethical standards. Therefore, prompts should clearly prohibit fictitious data, links to non-existent articles, or inaccurate citations.

Experienced specialists note that a good prompt for scientific material always specifies the model’s role: structuring assistant, language editor, example generator, but not the author of the study. This helps set realistic expectations and avoid misuse of the model. It’s also important to remember the originality requirements:

Clarify the model’s role: assistance with writing, editing, structuring, and template generation, but not conducting research.
Establish constraints: no fictitious data, no references to inaccessible sources, with an emphasis on the transparency of assumptions.
Separate requests: separate prompts for structure, style, examples, and conclusion formulation.

Authors use the chataibot.pro platform as a single access point to models that solve these tasks as part of the workflow. The service allows users to upload documents, submit them to the model, and receive suggestions for structuring, editing, and finalizing the text, which is convenient when preparing large-scale scientific papers. This type of assistance is especially useful for authors who combine research with a high workload and value the ability to partially automate publication preparation.